How to make a python Dataphram from the list

Create a dataframe from a list in Python: example code & explanation of how to convert a list to a dataframe.

The Python DataFrame is a powerful way to store, manipulate and analyze structured data. It is a two-dimensional data structure with labeled axes (rows and columns) and is similar to a spreadsheet. It is one of the most commonly used Python libraries for data analysis and manipulation.

Creating a DataFrame

A DataFrame can be created from various data sources such as lists, dictionaries, or ndarrays. To create a DataFrame from a python list, you can use the DataFrame() class in the pandas module. The following example creates a DataFrame from a list of dictionaries. Each dictionary represents a row in the DataFrame.


import pandas as pd

data = [{"name": "John", "age": 20}, 
        {"name": "Jane", "age": 25}, 
        {"name": "Sam", "age": 30}]

df = pd.DataFrame(data)
print(df)

The output of the above code will be:

   age  name
0   20  John
1   25  Jane
2   30   Sam

The DataFrame can also be created from a dictionary of lists. Each list represents the columns in the DataFrame and each dictionary key is the name of the column. The following example creates a DataFrame from a dictionary of lists:


data = {"name": ["John", "Jane", "Sam"], 
        "age": [20, 25, 30]}

df = pd.DataFrame(data)
print(df)

The output of the above code will be:

   age  name
0   20  John
1   25  Jane
2   30   Sam

The DataFrame can also be created from an ndarray. The ndarray must be two-dimensional and the column names and row indices must be specified. The following example creates a DataFrame from an ndarray:


import numpy as np

data = np.array([[20, "John"], [25, "Jane"], [30, "Sam"]])

df = pd.DataFrame(data, columns=["age", "name"])
print(df)

The output of the above code will be:

   age  name
0   20  John
1   25  Jane
2   30   Sam

The DataFrame can also be created from a CSV file. The CSV file must have a header row that specifies the column names. The following example creates a DataFrame from a CSV file:


df = pd.read_csv("mydata.csv")
print(df)

The output of the above code will be:

   age  name
0   20  John
1   25  Jane
2   30   Sam

The DataFrame can also be created from an SQL database. The SQL database must have a table with the column names and row data. The following example creates a DataFrame from an SQL database:


import sqlite3

conn = sqlite3.connect("mydb.db")
df = pd.read_sql_query("SELECT * FROM mytable", conn)
print(df)

The output of the above code will be:

   age  name
0   20  John
1   25  Jane
2   30   Sam

DataFrames can also be created from existing DataFrames. This is useful for merging data from multiple sources. The following example creates a DataFrame from another DataFrame:


data = [{"name": "John", "age": 20}, 
        {"name": "Jane", "age": 25}, 
        {"name": "Sam", "age": 30}]

df1 = pd.DataFrame(data)

data = [{"name": "Tom", "age": 21}, 
        {"name": "Jack", "age": 26}, 
        {"name": "Jill", "age": 31}]

df2 = pd.DataFrame(data)

df3 = pd.concat([df1, df2])
print(df3)

The output of the above code will be:

   age  name
0   20  John
1   25  Jane
2   30   Sam
0   21   Tom
1   26  Jack
2   31  Jill

The DataFrame is a powerful tool for manipulating and analyzing data in Python. It is a two-dimensional data structure with labeled axes (rows and columns) and is similar to a spreadsheet. It is one of the most commonly used Python libraries for data analysis and manipulation.

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